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name: ln_summary_protocol description: Build likelihood-free linear-nonlinear neural simulations and STA/firing-rate summaries for SNPE-style posterior recovery.

LN Summary Protocol

Use this skill when a recovery or experiment needs a small mechanistic neural simulator with summary features matching the paper's LN receptive-field example. Do not use it as a generic classifier or as a likelihood evaluator.

Inputs

• theta: receptive-field/filter vector.
• n_stimuli, dimension, and seed for deterministic stimulus generation.
• Optional bias and gain for Bernoulli spike probabilities.

Outputs

• A JSON-compatible item containing theta, stimuli, spikes, summary, sta, firing_rate, and metadata.

Workflow

1. Generate white-noise stimuli from a seeded RNG.
2. Apply a logistic nonlinearity to stimulus-filter projections.
3. Sample Bernoulli spikes without evaluating a likelihood.
4. Compute STA from spike-triggering stimuli and append firing rate.
5. Save the generated data item when used in recovery.

Validation

Run python tests/test_ln_summary.py from this skill directory.

Limitations

This is a reduced LN simulator for fast mechanism checks. It is not the full paper implementation and does not reproduce high-dimensional CNN/MDN experiments.